Dan’s COVID Charts

Created by Dan Goodspeed
using data from New York Times and
visualization help from Flourish.

Some have asked for ways to donate to help keep the charts updated and to continue making more similar charts. I'm not expecting much, but if you'd like, I made a few options. Thanks! -Dan

Paypal | GoFundMe | Patreon

This chart is a little different than the others. People wanted both cases and deaths- it has both. People wanted the chart to start in early March- this one does that. Having a chart that doesn't change as much from day to day (as each day only changes by 1/90th) allows the animation to run smoother and faster. And also, adding everything into one chart makes it easier to keep updated daily. I wanted a single metric that can best characterize the negative effect the virus is having on a population, with only the statistics of cases and deaths to work from. Deaths being most severe, and cases include all kinds of results, from death, to long-term or permanent organ damage, to just being sick for a long time, to no symptoms at all. What I ended up doing was averaging the (per million) daily normalized* deaths and cases for the given 90-day period (but counting deaths three times). The formula is likely to change in the future (and with it, the numbers), but at least for now, I'm using:

** Impact = (CASES + DEATHS * 3) / 4
I hope to add more factors in the future that will give a better representation of the impact. Keep checking back.

What you can take from the chart- When a number is going up, that state is doing worse than they did three months ago (going down means they're doing better). It's a very loose estimate, but you can also divide the number by 10,000 to get a percentage number of the population that at least got really sick from COVID during that three month period. Again, I'm still working on a better formula for a more accurate representation.

* "Normalization" (perhaps better called "smoothing") means the abnormalities in the data were evened out. For example, if there were 10 days in a row of a few cases/deaths a day and then one day of 1000... that looks awful and frenetic on a chart like this, even when framed in a per-week display. In reality, that 1000 is just a backlog catch-up, so I normalized it by spreading the thousand over previous dates for a more even / more realistic data. It works similarly when the total number of cases/deaths drops one day. Likely a correction from a previous report, I just subtracted the difference over previous dates to numbers that are probably closer to reality.